{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nKEoPoL0QYmDa8QtAiBDizXRlPAGHEMKiKEoHYIoQosoD6hM5jclZhBBcvXoV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2443bba34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#载入数据\n",
    "data = np.genfromtxt(\"LR-testSet2.txt\",delimiter=',')\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,test_size = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    #获取数据值所在的范围\n",
    "    x_min,x_max = x_data[:,0].min()-1,x_data[:,0].max()+1\n",
    "    y_min,y_max = x_data[:,1].min()-1,x_data[:,1].max()+1\n",
    "    \n",
    "    #生成网格矩阵\n",
    "    xx,yy = np.meshgrid(np.arange(x_min,x_max,0.02),\n",
    "                       np.arange(y_min,y_max,0.02))\n",
    "    \n",
    "    z = model.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "    z = z.reshape(xx.shape)\n",
    "    \n",
    "    #等高线图\n",
    "    cs = plt.contourf(xx,yy,z)\n",
    "    #画散点图\n",
    "    plt.scatter(x_test[:,0],x_test[:,1],c=y_test)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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S9cMHXn9B8eYkSdRyuhM0sy3xO9QxeEbWI0IIqzo4bgmwCl913hhCmJhLuSKd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2443bc96668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.72881355932203384"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree.fit(x_train,y_train)\n",
    "plot(dtree)\n",
    "dtree.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FhyW/S37pnWfxOUij8TlJhfSBV6dePzDP198ZzmGcHYkH71zcB3wJGJd63B+4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\nvIhIjP0/qjr9QyHhUrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2443bb24b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.77966101694915257"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators = 100)\n",
    "RF.fit(x_train,y_train)\n",
    "plot(RF)\n",
    "RF.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
